The present invention relates generally to computer vision and pattern recognition, and in particular to video analysis for detecting the presence of fire.
The use of video data to detect the presence of fire has become increasingly popular due to the accuracy, response time, and multi-purpose capabilities of video recognition systems. For instance, as opposed to a traditional particle detector, video detectors are capable of detecting the presence of fire prior to actual particles (e.g., smoke) reaching the detector.
In most applications, video-based fire detection systems trigger an alarm in response to the detection of fire (e.g., flame or smoke). However, in some applications the presence of either smoke or flame is expected and should not trigger an alarm. For example, the top of a smokestack emits smoke, detection of which should not result in the triggering of an alarm. Similarly, the top of a vent-stack emits a cloud of steam which may look like smoke and which should not result in the triggering of an alarm. Prior art systems have employed the use of regions of interest (ROI) or masks to either selectively process or ignore certain areas within a video detector's field of view to prevent false alarms such as this. In the smokestack example, a mask may be applied to the region surrounding the smokestack such that a video recognition system does not process or attempt to detect smoke in the masked region.
However, fixed ROIs or masks do not inherently account for the dynamic nature of smoke and flames. In particular, smoke exiting a smokestack may be pushed by ambient winds over a large portion of the field of view of a detector. To avoid false alarms, large areas of the field of view must be masked. Defining the mask or ROI for false alarm reduction, however, may result in missed detections in the large masked areas. A need therefore exists for a video-based fire detection system that can reduce false alarms and missed detections without requiring masking of large portions of the detectors field of view.